Compute Infrastructure Platforms Lead Data Engineer

• Ensure consumers are able to have a high level of trust in the analysis they produce based on the data our product line emits
• Generate data models for our team using firmwide tooling, linear algebra, statistics, and geometrical algorithms
• Deliver data collection, storage, access, and analytics data platform solutions in a secure, stable, and scalable way
• Implement database back-up, recovery, and archiving strategy
• Evaluate and report on access control processes to determine effectiveness of data asset security with minimal supervision
• Add to our team culture of diversity, opportunity, inclusion, and respect

Technologies:

• Marketing
• NoSQL
• Security
• DevOps
• Support

More:

hackajob is partnering directly with JPMorganChase to hire for this role. We are a global leader in financial services, providing strategic advice and products to the worlds most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. Our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion. Our Corporate Functions team covers a diverse range of areas from finance and risk to human resources and marketing, and our corporate teams are an essential part of our company, ensuring that were setting our businesses, clients, customers and employees up for success.

• Working knowledge of Infrastructure Platforms and Enterprise Server Operating Systems
• Working experience with both relational and NoSQL databases
• Experience and proficiency across the data lifecycle
• Experience with database back-up, recovery, and archiving strategy
• Proven experience working in SRE or Infrastructure Deployment roles, mid-level data pipeline or analytical product creation
• Proven data quality issue root cause analysis